The near-infrared (NIR) spectroscopy method, based on the near-infrared absorption spectra and the chemometrics technology, is mainly used for component sensing in human body (such as concentration of blood glucose). In the near-infrared region, the body fluid and soft tissue are relatively transparent and thus the light has a strong penetrability. In addition, the sensitivity, precision and reliability of quantitative analysis by the NIR spectroscopy method are significantly improved along with the great development of the chemometrics method and its applications in the near-infrared spectroscopy technology. The NIR spectroscopy method is recognized as the most promising technology for non-invasive detection in human body. An existing successful example is the non-invasive sensing of the blood oxygen saturation.
The near-infrared spectrum (with a wavelength ranging from 780 to 2500 nm) is caused by a double-frequency or combined-frequency absorption of vibration at a fundamental-frequency in the mid-infrared region of different compounds which contain chemical bonds such as C—H, O—H or N—H. The characteristics of the near-infrared spectra will change with the concentrations of organic compounds containing the chemical bond —H or its relative combined inorganic compounds. Thus, the concentration variation of the target component could be analyzed based on the spectrum feature. However, the near-infrared spectra of different components in the blood or tissue fluid overlap with each other to a great extent, as shown in FIG. 1. The process of spectrum detection is greatly influenced by the instrument drifts, temperature variations and background noises. Therefore, a high detection precision is difficult to acquire according to the calibration model built by the data measured at a single wavelength.
The near-infrared spectroscopy, combined with the multivariable analysis, is usually used for the qualitative detecting of material compositions, as shown in FIG. 2. Specifically, first, a series of calibration samples are designed to respectively have target component concentrations varied in a same interval, and then their near-infrared spectra are measured respectively. Next, the spectrum matrix and the concentration matrix of the samples are analyzed with the multivariable regression technology, and the calibration model is built to get the regression coefficient. Then, the near-infrared spectrum of the target sample with an unknown target component concentration is measured, and the spectrum matrix is analyzed and the concentration of the target component is conversely calculated based on the regression coefficient of the calibration model. As a most effective method, the PLS (Partial Least Square) is widely used for multivariable model calibration in the near-infrared spectroscopy analysis at present.
Though the near-infrared spectroscopy is one of the best methods to realize non-invasive sensing in human body, it is so complicated. Taking non-invasive blood glucose sensing as an example, the nonlinear scattering, which occurs when the near-infrared light passes through the dynamic changed tissue due to the complicated optical properties in the tissue, makes the glucose signal collection so difficult. In additional, the spectral signal caused by the variation of the blood glucose is very weak because of the small quantity of the blood glucose, and the absorption of the glucose itself overlaps the absorption of other components very badly. The strong absorption of water, protein and fattiness as well as the variation of body temperature and changes of other physiological conditions is the main interference factor. The key point of non-invasive blood glucose sensing by NIR is to collect the weak signal characterizing the variation of the glucose concentration from the badly overlapped backgrounds, which is influenced by various physiological factors.